The prostate gland is an essential part of the male reproductive system. Located in the pelvis behind the pubic bone and right in front of the rectum and below the neck of the bladder, the prostate completely surrounds the urethra, which is the passageway that carries urine from the bladder through the penis and out of the body. In a healthy adult male, the prostate is approximately the size and shape of a walnut, weighing about 20 grams and measuring about 3 cm in width and 2.8 cm in length. Partly fibromuscular and partly glandular, the prostate gland is divided into the following four regions: Anterior fibromuscular zone, peripheral zone, central zone, and transition zone (McNeal 1988). One of the functions of the prostate is to produce a thin and milky seminal fluid that mixes with the fluid produced by the seminal vesicles, a pair of glands attached to it, to make the semen. The sperm, carried from the testicles to the prostate through the vas deferens tube, mixes with the semen. The resulting fluid is then ejaculated during orgasm first by the ejaculatory ducts to the urethra and then through the urethra out of the body.
Prostate cancer occurs when cells of the prostate begin to grow and to multiply out of control. These cells may spread out of the prostate to the nearby lymph nodes, bones or other parts of the body. This spread is called metastasis. Prostate cancer is the most commonly diagnosed malignancy in men, and is found at autopsy in 30% of men at the age of 50, 40% at age 60, and almost 90% at age 90 (Garfinkel et al. 1994). Worldwide, it is the second leading cause of death due to cancer in men. At its early stages, the disease might not have any symptoms. Some men, however, might experience symptoms that could indicate the presence of prostate cancer. Some of these symptoms include frequent and burning urination, difficulty starting a urinary stream, difficulty in having an erection, and painful ejaculation. Since these symptoms could also indicate other diseases, these men would undergo screening for prostate cancer.
When diagnosed at an early stage, prostate cancer is curable. The purpose of screening is to detect prostate cancer at its early stages before the development of any symptoms. It can be performed using two tests: the prostate-specific antigen (PSA) blood test, and the digital rectal exam (DRE).
If the DRE finds an abnormality in the prostate, or the blood test reveals a high level of PSA, then a biopsy of the prostate is recommended. A biopsy is a surgical procedure that involves removing samples of prostate tissues for microscopic examination to determine if they contain cancer cells. In the transrectal biopsy, which is the most commonly used method, a hand-held biopsy gun with a spring-loaded slender needle is guided through the wall of the rectum into the prostate gland then quickly removed. This is done under transrectal ultrasound (TRUS) guidance—a procedure that uses ultrasound generated from a probe that is inserted into the rectum to create an image of the prostate gland. The biopsy needle will contain a cylinder of a prostate tissue sample used for histological examination. This is repeated several times; each biopsy resulting in a sample from a different area of the prostate. Despite the fact that many samples (around 12) are obtained, cancer can still be missed if none of the biopsy needles pass through the cancerous growth. Although this procedure is low-risk, complication may occur. Some possible treatable complications could include prolonged bleeding into the urethra, and infection of the prostate gland or urinary tract.
An appropriate treatment choice of the prostate cancer is based primarily on its stage, PSA level, and other factors like the man's age and his general health. Treatment options change considerably if the cancer has already spread beyond the prostate. The results of this project will be used in procedures to treat localized prostate cancer, which has four treatment options, (Bangma et al. 2001), of which include brachytherapy.
Brachytherapy is radiation therapy that is minimally invasive and that involves inserting seeds containing radioactive material directly into the prostate. LDR, or low-dose-rate brachytherapy, consists of inserting low-dose or low-energy seeds permanently into the prostate. HDR, or high-dose-rate brachytherapy, on the other hand, involves placing high-dose or high-energy seeds temporarily into the prostate, and then removing them once the desired radiation dose has been delivered. HDR provides better dose control, whereas LDR is simpler and with lower risk of infection since it doesn't involve a removal procedure (Nag S. 1997). Appropriate candidates for brachytherapy are men with cancer confined to the prostate gland.
Implantation techniques for prostate brachytherapy had started as early as 1913 by inserting radium through a silver tube that was introduced into the urethra. Brachytherapy evolved in the 1970's with Whitmore who invented an open implant technique that involved interstitial implantation using an open retropubic surgery. In 1980, Charyulu described a transperineal interstitial implant technique. Then, in 1983, Holm, an urologist from Denmark, invented the TRUS-guided technique for implanting permanent radioactive seeds transperineally into the prostate (Nag et al. 1997). This technique became very popular for many reasons including the fact that it is a minimally invasive, outpatient and one-time procedure (Nag et al. 1997).
Transperineal prostate brachytherapy employs TRUS as the primary imaging modality. Under TRUS guidance, a needle carrying the radioactive seeds is inserted through the perinium and into the prostate via a template.
A successful brachytherapy procedure requires several steps, including preoperative volume study using TRUS, computerized dosimetry, pubic arch interference determination since the pubic arch is a potential barrier to the passage of the needles, and postoperative dosimetric evaluation (Pathak et al. 2000). Image processing and specifically outlining the prostate boundary accurately plays a key role in all four steps (Grimm et al. 1994). In the volume study, the TRUS probe is inserted into the rectum to acquire a series of cross-sectional 2D images at a fixed interval from the base to the apex (Nag et al. 1997). Then, the prostate boundary is outlined on these cross-sectional 2D image slices using a segmentation algorithm.
Traditional segmentation algorithms involved a skilled technician to manually outline the prostate boundary. Although this provides an acceptable result (Tong et al. 1996), it is time consuming and is prone to user variability. Therefore, several semi-automatic and fully automatic algorithms, which can be classified into edge-based, texture-based and model-based, have been proposed for segmenting the prostate boundary from 2D TRUS images.
Edge-Based Prostate Boundary Detection Methods from 2D TRUS Images:
These algorithms first find image edges by locating the local peaks in the intensity gradient of the image, and then they outline the prostate boundary by performing edge selection followed by edge linking (Shao et al. 2003). Aamink et al. presented an edge-based algorithm for determining the prostate boundary using the gradient of the image in combination with a Laplace filter. They located possible edges at the zero crossings of the second derivative of the image and determined the edge strength by the value of the gradient of the image at that location. Then they used knowledge-based features and ultrasonic appearance of the prostate to choose the correct edges. Finally, they used adaptive interpolation techniques to link the edges that actually present a boundary (Aamink et al. 1994). This method gave good results but it could generate erroneous edges due to artifacts in the image (Shao et al. 2003).
Pathak et al. also used an edge-based method for outlining the prostate boundary from transrectal ultrasound images. First, they enhanced the contrast and reduced image speckle using the sticks algorithm (Pathak et al. 2000). Then, they further smoothed the resulting image using an anisotropic diffusion filter, and used prior knowledge of the prostate and its shape and echo pattern in ultrasonic images to detect the most probable edges. Finally, they overlaid the detected edges on the top of the image and presented them as a visual guide to the observers to manually delineate the prostate boundary (Pathak et al. 2000).
Liu et al. also used an edge-based technique called the radial “bas-relief” (RBR) method for prostate segmentation where they obtained a “bas-relief” image, which they superimpose on the original TRUS image to obtain the edge map (Liu et al. 1997). Its insensitivity to edges parallel to the radial direction from the centre of the prostate was the weakness of this method (Chiu et al. 2004). In addition, this method failed if the image centre and the prostate boundary centroid were far from each other.
Texture-Based Prostate Boundary Detection Methods from 2D TRUS Images
These techniques characterize regions of an image based on the texture measures. They can determine regions of the image with different textures, and create borders between them to produce an edge map. In their work, Richard and Keen presented an automatic texture-based segmentation method, which was based on a pixel classifier using four texture energy measures associated with each pixel in the image to determine the cluster it belongs to (Richard & Keen 1996). One of the drawbacks of this method was that the resulting prostate may be represented by a set of disconnected regions (Chiu et al. 2004).
Model-Based Prostate Boundary Detection Methods from 2D TRUS Images
These techniques use prior knowledge of 2D TRUS images of the prostate to delineate the prostate boundary efficiently. Some model-based methods are based on deformable contour models, in which a closed curve deforms under the influence of internal and external forces until a curve energy metric is minimized (Ladak et al. 2000). Other methods are based on statistical models, in which the variation of the parameters describing the detected object are estimated from the available segmented images and are used for segmentation of new images. The statistical models are obtained from a training set in an observed population (Shao et al. 2003).
Prater et al. presented a statistical model-based method for segmenting TRUS images of the prostate using feed-forward neural networks. They presented three neural network architectures, which they trained using a small portion of a training image segmented by an expert sonographer (Prater et al. 1992). Their method had the disadvantage of needing extensive training data.
Pathak et al. presented a method based on snakes to detect the prostate boundary from TRUS images. They first used the sticks algorithm to selectively enhance the contrast along the edges, and then integrated it with a snakes model (Pathak et al. 1998). This algorithm required the user to input an initial curve for each ultrasound image to initiate the boundary detection process. This algorithm is very sensitive to the initial curve and only works well when this initial curve is reasonably close to the prostate boundary (Shao et al. 2003).
Ladak et al. presented a model-based algorithm for 2D semi-automatic segmentation of the prostate using a discrete dynamic contour (DDC) approach. It involved using cubic spline interpolation and shape information to generate an initial model using only four user-defined points to initialize the prostate boundary. This initial model was then deformed with the DDC model (Ladak et al 2000).
Ladak's segmentation algorithm gave good results, demonstrating an accuracy of 90.1% and a sensitivity of 94.5%. In this approach, manual initialization required about 1 min, but the prostate segmentation procedure required about 2 seconds. (Ladak et al. 2000). However, this method requires careful manual initialization of the contour and further user interaction to edit the detected boundary, which introduces complexity and user variability.
Reducing or removing user interaction, and as a result the variability among observers, would produce a faster, more accurate and reproducible segmentation algorithm. In addition, it may remove the need for the user to initialize the segmentation procedure, a critical criteria for an intraoperative prostate brachytherapy procedure.
The present invention provides an automatic prostate boundary segmentation method that minimizes and/or eliminates user initialization for segmenting the prostate in 2D or 3D images; thereby reducing both the time required for the procedure and the inter/intra-operator variability ultimately improving the effectiveness and utility of medical in the diagnosis and treatment of prostate cancer.